Multiscale Hierarchical Decomposition Methods for Images Corrupted by Multiplicative Noise

被引:0
作者
Barnett, Joel [1 ]
Li, Wen [2 ]
Resmerita, Elena [3 ]
Vese, Luminita [1 ]
机构
[1] Univ Calif Los Angeles UCLA, Dept Math, Los Angeles, CA 90095 USA
[2] Fordham Univ, Dept Math, Bronx, NY 10458 USA
[3] Alpen Adria Univ Klagenfurt, Inst Math, Univ Str 65-67, A-9020 Klagenfurt, Austria
基金
奥地利科学基金会;
关键词
Image restoration; Multiplicative noise; Multiscale expansion; Ill-posed problem; TOTAL VARIATION MINIMIZATION; CONVEX VARIATIONAL MODEL; REMOVAL; REGISTRATION; SPECKLE;
D O I
10.1007/s10851-024-01220-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recovering images corrupted by multiplicative noise is a well-known challenging task. Motivated by the success of multiscale hierarchical decomposition methods (MHDM) in image processing, we adapt a variety of both classical and new multiplicative noise removing models to the MHDM form. On the basis of previous work, we further present a tight and a refined version of the corresponding multiplicative MHDM. We discuss existence and uniqueness of solutions for the proposed models and, additionally, provide convergence properties. Moreover, we present a discrepancy principle stopping criterion which prevents recovering excess noise in the multiscale reconstruction. Through comprehensive numerical experiments and comparisons, we qualitatively and quantitatively evaluate the validity of all proposed models for denoising and deblurring images degraded by multiplicative noise. By construction, these multiplicative multiscale hierarchical decomposition methods have the added benefit of recovering many scales of an image, which can provide features of interest beyond image denoising.
引用
收藏
页数:27
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